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IBM Data Science Experience

IBM Data Science Experience is IBM's collaboration platform for data scientists. It allows data scientist to create, work and share notebooks written in R, Python or Scala. IBM Data Science Experience was launched in September 2016 and was embedded as a core element within IBM's Watson DataWorks Project. The notebooks are based on Jupyter Open-Source software and offered as a managed service within IBM Cloud.

 
With Data Science Experience, IBM decided to go all-in on Open Source technologies and coding languages. IBM Data Science Experience offers RStudio as a managed service and Jupyter notebooks for Scala, Spark and R. The notebooks are - as in Jupyter - intuitive, capable to run line-by-line and easily shareable with colleagues and/or the community.

The vision of Data Science Experience is to create a common platform within (and beyond) an organization where all analysts and data-scientist work together in cracking analytical issues. It goes by the slogan: create, learn, collaborate.
 
Figure 1: The welcome menu within the IBM Data Science Experience
Image result for ibm data science experience
 
In the summer of 2016 below features were added:
  • Ability to deploy a job on a Apache Spark cluster within IBM Cloud: this allows to leverage Data Science Experience not only as a development interface but similarly to use it as a deployment environment where scripts can run 'on scale'.
  • Ability to share data within the community and thus to similarly use public data-sources within your data projects (e.g. weather data, economic data, etc.)
  • Access to relevant news and articles related to data science.
The coming-soon features of Data Science Experience are:
  • Ability to schedule jobs to daily run e.g. product recommendation scripts
  • Ability to bookmark articles and notebooks within the community
Access to the IBM Data Science Experience portal can be retrieved here. There is a 30-day trial period; default pricing runs at a cost-per-usage basis.
 
Contact us for more information on IBM Data Science Experience incl. trainings, how-to-get-started, best practices, etc.

 

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